The present disclosure relates generally to a model predictive control (MPC) system for a building heating, ventilation, and air conditioning (HVAC) system. The present disclosure relates more particularly to a MPC system for optimizing energy cost in a variable refrigerant flow (VRF) system including an outdoor subsystem and a plurality of indoor subsystems.
Commercial buildings consume approximately 20% of the total U.S. energy consumption and account for roughly $200 billion per year in primary energy expenditures. The Energy Information Administration projects that commercial floor space and primary energy consumption will continue to grow into the future. Average energy prices, on the other hand, are expected to remain relatively stable. Therefore, the amount spent on energy in commercial buildings will continue to increase significantly. Given the significance of these energy cost values and their projected growth, buildings have become a prime target for control strategies designed to reduce consumption or improve efficiency, particularly in the area of temperature control.
Many HVAC systems in commercial buildings and educational facilities use simple on/off and proportional-integral-derivative (PID) controllers for control of their equipment. They rely on temperature controllers whose only goal is to converge to the desired temperature set point and stay there, within some tolerance. However, a better goal is to minimize total energy consumption or minimize total energy cost. In a utility market with time-varying prices, there exists the potential for cost savings by temporally shifting heating or cooling loads using some form of energy storage. To achieve these savings, predictive optimization can be used with a model of the system to forecast the future load. Load shifting decreases the burden on power plants during peak hours, allowing them to operate more efficiently. Furthermore, chillers operate more efficiently at night when the cooling water temperature is lower.
MPC is a method of advanced process control that has been highly successful over the past two decades. MPC uses a model of the system that relates the inputs (control actions) to outputs (process measurements). The model is used to predict the process variables based on the actions taken by the controller over a period of time called the horizon. At each step, MPC solves an online optimization problem using this model to determine a sequence of control actions that achieve an objective such as minimizing tracking error or input usage while respecting process constraints such as equipment capacity and safety bounds. The first control action in the sequence is implemented and the optimization problem is solved again at the next step after new measurements are obtained. In economic MPC, the objective in the optimization problem is to minimize total cost.
Economically optimal control systems have not been deployed widely in the HVAC industry. One fundamental obstacle to the successful deployment of MPC in HVAC systems is the large number of building zones. To implement MPC in HVAC systems, it may be desirable to solve the optimization problem in a reasonably short time (e.g., on the order of a few minutes). Campus-wide implementations may contain hundreds of buildings and zones. A single, combined control system for these applications is impractical and undesirable since the resulting single optimization problem is too large to solve in real time.